I'm a research engineer working on large-scale multimodal AI systems that see, reason, and operate under real-world constraints. My work spans computer vision, vision-language models, reinforcement learning, distributed training, and more recently, music/audio modeling.

I enjoy building systems that move beyond benchmarks — from autonomous navigation to distributed inference pipelines — and occasionally making robots smarter (or at least slightly less confused).

• BetterSearch
  Desktop application integrating LLMs, advanced RAG, and native OS functionality for semantic file search. Supports text-to-SQL via osquery, CPU/GPU customization, and local/cloud deployment modes.

• Exo
  Distributed inference framework for running LLMs across heterogeneous device clusters. Contributed AMD GPU support improvements and llama.cpp Windows backend integration.

• VidTune
  Generative AI web app for tailored music creation for videos, with genre, tempo, keyword, and dynamic mixing customization.

• Unified Local-Cloud Decision-Making (UniLCD)
  Residual reinforcement learning framework for cloud-edge collaboration in embodied vision systems. Presented at ECCV 2024.

• Autonomous Underwater Vehicle
  Led team to Singapore AUV Challenge finals. Built navigation, perception, and embedded systems; integrated enhanced underwater vision algorithms.

• ACL 2026 (Under Review):
  WellLogBench: A Domain-Expert Curated Benchmark for Evaluating LLM Reasoning on Subsurface Well Log Data

• ECCV 2024:
  Unified Local-Cloud Decision-Making via Residual Reinforcement Learning

• ETCCS 2020:
  Person Re-Identification by Analyzing Dynamic Variations in Gait Sequences

• Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) — Research Engineer I (Aug 2025 – Present, Abu Dhabi, UAE)
  Training and post-training multimodal LLMs and VLMs for domain-specific reasoning (Multimodal Energy GPT).

  • 70B-class model SFT and DPO across multi-node GPU clusters
  • Distributed inference for 235B models (3B+ tokens/day, 32 nodes)
  • Synthetic data generation and large-scale evaluation pipelines

• LossFunk (Initialize Program) — Researcher (June 2025 – Present)
  Independent research on MIDAS, a modular framework for music source separation.

• Human-to-Everything (H2X) Lab, Boston University — Research Engineer / Graduate Research Assistant (Mar 2023 – Mar 2025)
  Vision-language models for autonomous driving, cloud-edge routing, energy-aware navigation, reinforcement learning, and embodied AI systems.

• Robert Bosch GmbH — Research Intern (Jan 2020 – June 2020)
  Open-set classification and predictive maintenance for vehicular systems.

• Center for Development of Advanced Computing (C-DAC) — Research Intern (May 2019 – Oct 2019)
  Gait-based person re-identification (91.13% accuracy on CASIA-B).

• Ottometric Inc. — Software Engineering Intern (June 2023 – Aug 2023)
  Submodular optimization for dataset summarization and efficient model training.

• Ignitarium Technology Solutions — AI Engineer (Jan 2022 – July 2022)
  High-performance INT8 kernel development for accelerator hardware (30% inference improvement).

• Synopsys India — Software Engineer (Jan 2021 – Jan 2022)
  Apache Spark pipelines, BERT-based anomaly detection, unified production log systems.

• Thermo Fisher Scientific — Summer Intern (May 2018 – July 2018)
  Embedded C++ development on ARM hardware; Linux Yocto + Qt systems.

• Boston University
  M.S. in Computer Science (Sep 2022 – May 2024)
  Thesis: Efficient Vision and Language Models for Autonomous Systems

• IIITDM Kancheepuram
  B.Tech. + M.Tech. in Electronics and Communication Engineering (July 2015 – June 2020)

• Website: tensorsofthewall.com
• GitHub: github.com/tensorsofthewall
• LinkedIn: linkedin.com/in/sandeshbharadwaj97

“Hurricane Sandy” wasn’t a weather event — it was me, my spiky hair, and a guitar solo on stage.

Teaching machines to see.
Scaling models responsibly.
Occasionally arguing with SLURM.